Semantic Search

Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query.[1] Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. https://en.wikipedia.org/wiki/Semantic_search

Semantic search is powered by vector search, which enables semantic search to deliver and rank content based on context relevance and intent relevance. Vector search encodes details of searchable information into fields of related terms or items, or vectors, and then compares vectors to determine which are most similar. A vector search-enabled semantic search produces results by working at both ends of the query pipeline simultaneously: When a query is launched, the search engine transforms the query into embeddings, which are numerical representations of data and related contexts. They are stored in vectors. The kNN algorithm, or k-nearest neighbor algorithm, then matches vectors of existing documents (a semantic search concerns text) to the query vectors. The semantic search then generates results and ranks them based on conceptual relevance.